updated docs and added benchmark for node_redundancy

benchmarks/benchmarks/bench_bipartite.py

@@ -0,0 +1,24 @@
+from .common import (
+    backends,
+    edge_prob,
+    Benchmark,
+)
+import networkx as nx
+
+# for an unbalanced bipartite random graph
+n = [50, 100, 200, 400, 800]
+m = [25, 50, 100, 200, 400]
+
+
+class Redundancy(Benchmark):
+    params = [(backends), (n), (m), (edge_prob)]
+    param_names = ["backend", "n", "m", "edge_prob"]
+
+    def time_node_redundancy(self, backend, n, m, edge_prob):
+        G = get_random_bipartite_graph(n, m, edge_prob)
+        _ = nx.node_redundancy(G, backend=backend)
+
+
+def get_random_bipartite_graph(n, m, p, seed=42, directed=False):
+    """Ref. https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.bipartite.generators.random_graph.html"""
+    return nx.bipartite.random_graph(n, m, p, seed, directed)

nx_parallel/algorithms/bipartite/redundancy.py

@@ -8,8 +8,10 @@
 
 
 def node_redundancy(G, nodes=None):
-    """Computes the node redundancy coefficients for the nodes in the bipartite
-    graph `G`."""
+    """In the parallel implementation we compute the node redundancy coefficients
+    for all the nodes in the bipartite graph `G` concurrently.
+
+    networkx.bipartite.node_redundancy : https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.bipartite.redundancy.node_redundancy.html"""
 
     if hasattr(G, "graph_object"):
         G = G.graph_object